Department of Electrical Engineering and Information Technologies, University of Naples Federico II, Via Claudio 21, 80125 Naples, Italy.
Sensors (Basel). 2023 Jul 6;23(13):6200. doi: 10.3390/s23136200.
A heartbeat generates tiny mechanical vibrations, mainly due to the opening and closing of heart valves. These vibrations can be recorded by accelerometers and gyroscopes applied on a subject's chest. In particular, the local 3D linear accelerations and 3D angular velocities of the chest wall are referred to as seismocardiograms (SCG) and gyrocardiograms (GCG), respectively. These signals usually exhibit a low signal-to-noise ratio, as well as non-negligible amplitude and morphological changes due to changes in posture and the sensors' location, respiratory activity, as well as other sources of intra-subject and inter-subject variability. These factors make heartbeat detection a complex task; therefore, a reference electrocardiogram (ECG) lead is usually acquired in SCG and GCG studies to ensure correct localization of heartbeats. Recently, a template matching technique based on cross correlation has proven to be particularly effective in recognizing individual heartbeats in SCG signals. This study aims to verify the performance of this technique when applied on GCG signals. Tests were conducted on a public database consisting of SCG, GCG, and ECG signals recorded synchronously on 100 patients with valvular heart diseases. The results show that the template matching technique identified heartbeats in GCG signals with a sensitivity and positive predictive value (PPV) of 87% and 92%, respectively. Regression, correlation, and Bland-Altman analyses carried out on inter-beat intervals obtained from GCG and ECG (assumed as reference) reported a slope of 0.995, an intercept of 4.06 ms (R > 0.99), a Pearson's correlation coefficient of 0.9993, and limits of agreement of about ±13 ms with a negligible bias. A comparison with the results of a previous study obtained on SCG signals from the same database revealed that GCG enabled effective cardiac monitoring in significantly more patients than SCG (95 vs. 77). This result suggests that GCG could ensure more robust and reliable cardiac monitoring in patients with heart diseases with respect to SCG.
心跳会产生微小的机械振动，主要是由于心脏瓣膜的开闭。这些振动可以通过贴在受检者胸部的加速度计和陀螺仪来记录。具体来说，胸部的局部三维线性加速度和三维角速度分别被称为心震图（SCG）和心旋图（GCG）。这些信号通常具有较低的信噪比，并且由于姿势和传感器位置的变化、呼吸活动以及其他的个体内和个体间可变性来源，其幅度和形态会发生不可忽略的变化。这些因素使得心跳检测成为一项复杂的任务；因此，在 SCG 和 GCG 研究中通常会获取参考心电图（ECG）导联，以确保正确定位心跳。最近，一种基于互相关的模板匹配技术已被证明在识别 SCG 信号中的单个心跳方面特别有效。本研究旨在验证该技术在 GCG 信号中的应用性能。该研究在一个由 100 名患有瓣膜性心脏病患者的同步记录的 SCG、GCG 和 ECG 信号组成的公共数据库上进行了测试。结果表明，模板匹配技术可以识别 GCG 信号中的心跳，其灵敏度和阳性预测值（PPV）分别为 87%和 92%。对从 GCG 和 ECG（视为参考）获得的心跳间期进行回归、相关和 Bland-Altman 分析，得到的斜率为 0.995，截距为 4.06ms（R>0.99），皮尔逊相关系数为 0.9993，一致性界限约为±13ms，偏差可忽略不计。与同一数据库中的 SCG 信号的先前研究结果进行比较表明，与 SCG 相比，GCG 可使更多的患者实现有效的心脏监测（95 例比 77 例）。这一结果表明，与 SCG 相比，GCG 可以确保患有心脏病的患者进行更稳健、更可靠的心脏监测。
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